Data-Driven State of Charge Estimation of Li-ion Batteries using Supervised Machine Learning Methods

Abstract

Recently, electrical vehicles (EVs) have attracted considerable attention from researchers due to the transition of the transportation industry and the increasing demand in the clean energy domain. State of charge (SOC) of Li-ion batteries has a significant role in improving the efficiency, performance, and reliability of EVs. Estimating the SOC of the Li-ion battery cannot be done directly from inner measurements due to the complex and dynamic nature of these kinds of batteries. Several data-driven approaches have recently been used to estimate the SOC of Li-ion batteries, benefiting from the availability of battery data and hardware computing capacity. However, selecting the discriminative features and best supervised machine learning (ML) models for accurate battery states estimation is still challenging. Thus, this paper investigates the effect of different ML models and extracted input features of Li-ion batteries, including Electrochemical Impedance Spectroscopy (EIS) and multi-channel feature set on the SOC prediction. The results on the public Panasonic dataset indicate that using EIS feature set as an input to the deep neural network (DNN) model is more efficient than the multi-channel feature set. Moreover, the DNN model outperforms the Gaussian process regression (GPR) model in terms of the mean squared error, mean absolute error, and root mean squared error rates for the SOC prediction.